<![CDATA[Automated hospitality services raise questions about transparency when guests first encounter artificial intelligence (AI) and when systems cannot resolve their requests. This structured evidence review examines AI disclosure, human escalation, trust, and organizational accountability. An access-limited synthesis mapped 19 empirical reports published between 2019 and July 2026, supplemented by relevant reviews and conceptual studies. Diverse methods, incomplete full-text access, and the absence of independent screening precluded meta-analysis and a comprehensive risk-of-bias assessment. The available evidence suggests that disclosure effects depend on service criticality and failure conditions, while recovery evaluations vary with problem severity, agent capabilities, and guest preferences. Responsibility assigned to a robot also differs from that attributed to its service organization. We develop a proposed executable transparency framework linking identity and capability information, accessible human assistance, information continuity, and clearly assigned recovery authority. Five propositions specify how these features could be examined in future research. The framework is conceptual and has not been tested as an integrated model. Because the evidence search and appraisal were incomplete, the findings constitute a bounded synthesis rather than definitive systematic-review conclusions.]]>
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